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Hotzone identification with GIS-based post-network screening analysis

机译:基于GIS的网络后筛选分析识别热区

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This paper proposes a two-step method called post-network screening analysis for identifying collision hotzones (i.e., groups of neighboring hotspots) on a road network. The first step is the familiar safety network screening that uses AASHTO's Highway Safety Manual (HSM) to identify and rank individual locations (i.e., hotspots) according to their potential for safety improvement. The second step is new and involves network-constrained kernel density estimation (KDE), a type of spatial analysis. KDE uses expected collision counts to estimate collision density, and outputs a graphical display that shows areas with a high density of expected collision counts. These are the hotzones. A particularly interesting area of application is the identification of high-collision corridors that may benefit from a program of systemic safety improvements. The proposed method was tested using five years of collision data (2005-2009) for the City of Regina, Saskatchewan. Three different network screening measures were compared: observed collision counts, observed severity-weighted collision counts, and expected severity-weighted collision counts. The study found that observed severity-weighted collision counts produced a dramatic picture of the city's hotzones, but this picture could be misleading as it could be heavily influenced by a small number of severe collisions. The results obtained from the expected severity-weighted collision counts smoothed the effects of the severity-weighting and successfully reduced regression-to-the-mean bias. A comparison is made between the proposed approach and the results of the HSM's existing network screening method. As the proposed approach takes the spatial association of roadway segments into account, and is not limited to single roadway segments, the hotzones identified capture a higher number of expected equivalent-property-damage-only (EPDO) collisions than the existing HSM methodology. The study concludes that the proposed two-step method can help transportation safety professionals to prioritize hotzones within high-collision corridors more efficiently and scientifically.
机译:本文提出了一种称为后网络筛选分析的两步方法,用于识别道路网络上的碰撞热点区域(即相邻热点的组)。第一步是熟悉的安全网络筛选,它使用AASHTO的《公路安全手册》(HSM)来根据单个位置(即热点)的安全改进潜力来识别和排序。第二步是新步骤,涉及网络约束的内核密度估计(KDE),这是一种空间分析。 KDE使用预期的碰撞计数来估计碰撞密度,并输出图形显示,以显示具有较高的预期碰撞计数密度的区域。这些是热点地区。一个特别有趣的应用领域是识别高碰撞走廊,这可能会受益于系统安全性改进计划。使用萨斯喀彻温省里贾纳市的五年碰撞数据(2005年至2009年)对提出的方法进行了测试。比较了三种不同的网络筛选措施:观察到的碰撞计数,观察到的严重性加权碰撞计数和预期的严重性加权碰撞计数。研究发现,观察到的严重性加权碰撞计数对城市的热点地区产生了戏剧性的描述,但是由于受到少量严重碰撞的严重影响,该图像可能会产生误导。从预期的严重性加权碰撞计数获得的结果使严重性加权的影响变得平滑,并成功减少了均值回归。在提议的方法和HSM现有的网络筛选方法的结果之间进行了比较。由于所提出的方法考虑了道路段的空间关联,并且不仅限于单个道路段,因此与现有的HSM方法相比,所识别的热点区域捕获的碰撞次数更多,且预期的仅等效物性损害(EPDO)碰撞次数更多。研究得出结论,所提出的两步法可以帮助运输安全专业人员更高效,更科学地确定高碰撞走廊内的热点区域。

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